We tackle the aforementioned challenges by adopting a Siamese neural network model, which was originally introduced in image matching [1]} and then has been actively applied to various pattern analyses [2]}, [3]}, [4]}. In our work, we take advantage of the “homophily principle" in social networks, the tendency of an individual node to associate with similar others [5]}, and adopt the Siamese neural network model with the primary role of dimensionality reduction. To this end, we learn embeddings that accurately capture the homophily effect of information diffusion networks. Since a trained Siamese neural network inductively maps new input data (i.e., unexplored nodes) whose connectivity information to the training data (i.e., explored nodes) is unknown, it can easily be applied to our problem. Fig. REF  illustrates the architecture of our Simamese neural network for inductively predicting edges \(\Theta ^{(t)}\) . As depicted in the figure, our Siamese neural network consists of twin multilayer perceptron (MLP) networks to which two feature vectors \({\bf x}_u\)  and \({\bf x}_v\)  associated with nodes \(u\)  and \(v\) , respectively, are fed separately. The MLPs act as encoders that compute embedding vectors, denoted as \({\bf e}_u\)  and \({\bf e}_v\)  for nodes \(u\)  and \(v\) , respectively. Finally, similarly as in [6]}, we compute the Hadamard product of two embedding vectors and then utilize a sigmoid output layer to infer \(\theta _{uv}^{(t)}\)  from the product. That is, \(\theta _{uv}^{(t)} = \text{sigmoid}({\bf e}_u \odot {\bf e}_v)\) , where \(\odot \)  denotes the Hadamard product.
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